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Abstract

The sensitivity of tree growth to precipitation regulates their responses to drought, and is a crucial metric for predicting ecosystem dynamics and vulnerability. Sensitivity may be changing with continuing climate change, yet a comprehensive assessment of its change is still lacking. We utilized tree ring measurements from 3,044 sites, climate data and CO2 concentrations obtained from monitoring stations, combined with dynamic global vegetation models to investigate spatiotemporal changes in the sensitivity over the past century. We observed an increasing sensitivity since around 1950. This increased sensitivity was particularly pronounced in arid biomes due to the combined effect of increased precipitation and elevated CO2. While elevated CO2 reduced the sensitivity of the humid regions, the intensified water pressure caused by decreased precipitation still increased the sensitivity. Our findings suggest an escalating vulnerability of tree growth to precipitation change, which may increase the risk of tree mortality under future intensified drought.

Abstract

Negative Air Ions (NAIs), essential for environmental and human health, facilitate air purification and offer antimicrobial benefits. Our study achieves hourly estimations of NAIs using a machine learning framework, developed from a multi-layer selection pipeline of over 200 variables, to identify the key determinants critical for adapting to high-resolution NAIs dynamics. Addressing site sparsity and NAIs volatility, we introduced a hybrid stacking incorporating pseudo sites generated from Kriging Spatiotemporal Augmentation (KSTA) to mitigate spatial overfitting. Our approach, validated in Zhejiang, China, demonstrates exceptional accuracy, achieving R 2 values of 0.90 (sample-based), 0.85 (temporal-based), and 0.79 (site-based). This work not only sheds light on NAIs behavior in relation to diurnal shifts, land use, and environmental events, but also integrates a health grading system, enhancing public health strategies through precise air quality assessment.

Abstract

In recent years, it has been found that magnetic dip caused by diamagnetic motion of injected plasma can provide an appropriate environment for excitation of electromagnetic ion cyclotron (EMIC) waves. These findings have been widely reported in the Earth's magnetic environment. However, it has rarely been reported in Jupiter's magnetic environment. This paper reports the characteristics of EMIC waves observed by Juno in the magnetic dip of Jupiter. Multiple-band EMIC waves are observed in frequency range from 10−3 Hz to several Hz. The theoretical analysis shows that in this event both He+ band and O+ band EMIC waves can be constrained in the magnetic dip, which is consistent with the wave emissions observed inside the magnetic dip. Our result provides the first evidence that EMIC wave can be ducted inside a magnetic dip in Jupiter's magnetosphere.

Abstract

On 18 November 2023, SpaceX launched the Starship, the tallest and the most powerful rocket ever built. The Super Heavy engine separated from the Starship spacecraft and exploded at 90 km of altitude, while the main core Starship continued to rise up to 149 km and exploded after ∼8 min of flight. In this work, we used data from ground-based GNSS receivers and we analyzed total electron content (TEC) response to the Starship flight and the two explosions. For the first time, we observed large-distance northward propagation of intensive 2,000 km V-shaped ionospheric disturbances from the rocket trajectory. The observed perturbations, most likely, represent shock waves propagating with the cone angle of ∼14° on the North and ∼7° on the South against the flight track that corresponds to the Mach angle of the shock waves in the lower atmosphere. The Starship explosion also produced a non-chemical depletion in the ionospheric TEC.

Abstract

The Arctic rapidly warms and sea ice retreats, a large fraction of organic carbon (OC), currently stored in coastal permafrost will be released into the marine system. Once reintroduced into the active carbon cycle, this material will either be decomposed or buried on the shelf depending on its hydrodynamic and chemical properties. Currently, carbon estimates are based on bulk measurements, which does not take the hydrodynamic pathway of different fractions into account. Therefore, eight coastal permafrost locations have been sampled along the Canadian Beaufort Sea Coast, hydrodynamically fractionated and analyzed for their C, N, 13C and 14C content. We found that the matrix-free fraction (low density <1.8 g/cm3, and high-density >1.8 g/cm3; <38 μm) account for 77%–98% of the OC. By using a coastal classification combined with field data, our results showed that short coastal segments can become key players in delivering matrix-free, easily degradable OC to the marine system.

High-altitude balloon-launched uncrewed aircraft system measurements of atmospheric turbulence and qualitative comparison with infrasound microphone response
Anisa N. Haghighi, Ryan D. Nolin, Gary D. Pundsack, Nick Craine, Aliaksei Stratsilatau, and Sean C. C. Bailey
Atmos. Meas. Tech., 17, 4863–4889, https://doi.org/10.5194/amt-17-4863-2024, 2024
This work summarizes measurements conducted in June 2021 using a small, uncrewed, stratospheric glider that was launched from a weather balloon to altitudes up to 30 km above sea level. The aircraft conducted measurements of wind speed and direction, pressure, temperature, and humidity during its descent as well as measurements of infrasonic sound levels. These data were used to evaluate the atmospheric turbulence observed during the descent phase of the flight.

Estimating hourly ground-level aerosols using GEMS aerosol optical depth: A machine learning approach
Sungmin O, Ji Won Yoon, and Seon Ki Park
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-142,2024
Preprint under review for AMT (discussion: open, 0 comments)
Air pollutants such as PM10 or PM2.5 can cause adverse public health and environment effects, therefore their regular monitoring is crucial to keep the pollutant concentrations under control. Our study demonstrates the potential of high-resolution aerosol optical depth (AOD) data from the Geostationary Environment Monitoring Spectrometer (GEMS) satellite to estimate ground-level PM concentrations using a machine learning model. 

Abstract

Precipitation δ18O has offered valuable insights into the evolution of the Asian monsoon. Recent researches focusing on precipitation Δ′17O has enhanced our understanding by offering new perspectives beyond those of δ18O, revealing insights into vapor sources and continental recycling. Nevertheless, there remains a lack of interannual triple oxygen isotope data, particularly in the Asian monsoon region. In this study, we analyzed the triple oxygen isotopes and hydrogen isotopes in monthly precipitation samples collected from Chongqing in Southwest China between 2019 and 2022 A.D. Seasonal variations in δD, δ18O, δ17O, and d-excess values were observed, with lower values during the rainy season and higher values during the dry season, highlighting the impact of changes in moisture sources and local meteorological conditions on seasonal shifts in δD, δ18O, and δ17O. While, mean Δ′17O values were higher in rainy season and lower in dry season. Notably, during rainy season, there is a negative correlation between monthly Δ′17O values and the RH of the vapor source area, as well as a positive correlation with d-excess. Recalculated Δ′17O values based on RH of oceanic moisture source, are higher than the measured values for this period, indicating the contribution of terrigenous moisture to precipitation in SW China. Precipitation Δ′17O values provide a more precise reflection of changes in moisture source, continental recycling, and evapotranspiration processes that drive water cycling compared Integrating modeling works in future will facilitate the use of precipitation Δ′17O values to quantify the impact of different moisture source on precipitation.

No abstract is available for this article.

The grid-level fixed asset model developed for China from 1951 to 2020
Danhua Xin, James Edward Daniell, Zhenguo Zhang, Friedemann Wenzel, Shaun Shuxun Wang, and Xiaofei Chen
Nat. Hazards Earth Syst. Sci. Discuss., https//doi.org/10.5194/nhess-2024-138,2024
Preprint under review for NHESS (discussion: open, 0 comments)
A high-resolution fixed asset model can help improve the accuracy of earthquake loss assessment. We develop a grid-level fixed asset model for China from 1951 to 2020. We first compile the provincial-level fixed asset from yearbook-related statistics. Then, this dataset is disaggregated into 1 km*1 km grids by using multiple remote sensing data as the weight indicator. We find that fixed asset value increased rapidly after the 1980s and reached 589.31 trillion Chinese yuan in 2020.

Abstract

In this study, we simulate the Solar Wind Charge Exchange (SWCX) soft X-ray emissions at dayside magnetosheath and cusps by using magnetohydrodynamic (MHD) and LAtmos TEst Particle (LaTeP) models. MHD models are unable to resolve the particle kinetic effects, such as the different behaviors of ions with different q/m, or distinguish the magnetospheric plasma from the solar wind plasma. We investigate these effects with the LaTeP model. As the LaTeP model does not self-compute magnetic and electric field, the magnetic and electric field data obtained from Open Geospace General Circulation Model (OpenGGCM) and Lagrangian version of the piecewise parabolic method (PPMLR) MHD model are used as the input to LaTeP model. The soft X-ray emissivity maps simulated from pure OpenGGCM and PPMLR MHD approaches and from LaTeP-OpenGGCM and LaTeP-PPMLR approaches are presented and compared. The results indicate that the LaTeP model can well resolve the kinetic effects and can be used to investigate the individual spectral characteristics. Therefore, the LaTeP model is a complementary approach for simulating the X-ray emissions near the dayside magnetopause. We also calculate the ratio of integrated OVII/OVIII line intensities, produced by charge exchange of O7+ ions and O8+ ions, respectively. We find a relatively higher ratio at the bow shock compared to the surrounding areas, suggesting that this ratio can be an effective parameter to identify the bow shock location.

Abstract

Carbon and nitrogen are considered as candidate light elements present in planetary cores. However, there is limited understanding regarding the structure and physical properties of Fe-C-N alloys under extreme conditions. Here diamond anvil cell experiments were conducted, revealing the stability of hexagonal-structured Fe7(N0.75C0.25)3 up to 120 GPa and 2100 K, without undergoing any structural transformation or dissociation. Notably, the thermal expansion coefficient and Grüneisen parameter of the alloy exhibit a collapse at 55–70 GPa. First-principles calculations suggest that such anomaly is associated with the spin transition of iron within Fe7(N0.75C0.25)3. Our modeling indicates that the presence of ∼1.0 wt% carbon and nitrogen in liquid iron contributes to 9–12% of the density deficit of the Earth's outer core. The thermoelastic anomaly of the Fe-C-N alloy across the spin transition is likely to affect the density and seismic velocity profiles of (C,N)-rich planetary cores, thereby influencing the dynamics of such cores.

Abstract

We extend the previous three-dimensional (3D) empirical reconstruction (ER) model for a set of ideal magnetohydrodynamics (MHD) constraints into a resistive MHD 3D ER model that includes additional resistive MHD constraints and additional measurements from NASA's Magnetospheric Multiscale (MMS) mission. The same form of a stochastic optimization algorithm is used as in the previous ideal MHD 3D ER model to directly minimize the loss function that includes a few more highly nonlinear terms characterizing the model-measurement differences and the model departures from physical constraints. The resistive MHD 3D ER model is applied to three regions of MMS measurements that correspond to direct sampling of an electron diffusion region (EDR), a region adjacent to the EDR, and one far away from the EDR. The reconstructed plasma and electromagnetic fields are of high quality in all three regions as measured by model-measurement difference indices and physics-based quality indicators. The reconstructed fields in the EDR provide us with a good view of the spatial configuration of the reconnection site. We specifically examine the effect of resistivity on energy exchange in the vicinity of the EDR. It was discovered that in the EDR, the energy exchange shows an exclusive and systematic one-channel process between the plasma thermal energy and electromagnetic energy with the conversion rate highly correlated with the strength of the turbulent electromagnetic fields. In the other two regions away from the EDR, the energy exchange between the electromagnetic energy and the plasma thermal and kinetic energies shows rapidly-varying and random characteristics.

Abstract

At mid-latitudes, a narrow band of near-surface wind convergence (NSWC) overlies the western boundary currents in long-term climatology as a response to steep sea surface temperature gradients. The underlying dynamics shaping mean convergence in the mid-latitude region have been investigated in detail. In polar regions, surface temperature gradients are intense along the sea ice edges. However, literature concerning NSWC near sea ice edges is limited. This study investigates time-mean NSWC along sea ice edges and its shaping processes, focusing on the Greenland Sea, based on atmospheric reanalysis. In cold-season climatology, positive NSWC overlies the sea ice edge, resulting in a localized upward motion reaching the free atmosphere. The mean NSWC was insensitive to sea ice thickness and surface roughness in the regional model. This study suggests that, in addition to local atmospheric boundary processes, extreme NSWC events play a vital role in shaping the mean distribution. Although these features are similar to those along the Gulf Stream, atmospheric fronts appear to play a relatively minor role in the Greenland Sea. Instead, the frequent cyclone generation near the sea ice edge and the anticyclonic circulation over Greenland in conjunction with the transient synoptic circulation seem essential. In the warm season, positive NSWC was virtually missing in the Greenland Sea, unlike in the Gulf Stream region, reflecting the shallow virtual temperature response to the surface thermal forcing. This study contributes to understanding the mechanisms by which sea ice variability affects large-scale atmospheric circulation in remote regions.

Abstract

Tropical wetlands account for ∼20% of the global total methane (CH4) emissions, but uncertainties remain in emission estimation due to the inaccurate representation of wetland spatiotemporal variations. Based on the latest satellite observational inundation data, we constructed a model to map the long-term time series of wetland extents over the Sudd floodplain, which has recently been identified as an important source of wetland CH4 emissions. Our analysis reveals an annual, total wetland extent of 5.73 ± 2.05 × 104 km2 for 2003–2022, with a notable accelerated expansion rate of 1.19 × 104 km2 yr−1 during 2019–2022 driven by anomalous upstream precipitation patterns. We found that current wetland products generally report smaller wetland areas, resulting in a systematic underestimation of wetland CH4 emissions from the Sudd wetland. Our study highlights the pivotal role of comprehensively characterizing the seasonal and interannual dynamics of wetland extent to accurately estimate CH4 emissions from tropical floodplains.

Abstract

Tectonic landforms and surficial lithologic age are essential data for producing quality late Quaternary fault maps and predicting coseismic fault rupture location before an earthquake. However, we lack a clear understanding of the relationship between tectonic landforms and shallow earthquake processes and how lithologic age relates to landform preservation. We assess how fault location error (rupture-to-fault separation distance) and coseismic displacement residual (difference between observed and predicted coseismic displacement) vary with tectonic landform and lithologic age for four historical earthquakes. Certain tectonic landforms identified before these earthquakes correlate with lower fault location errors and median displacements below model predictions. Faults cutting Holocene units exhibit the largest location errors, reflecting surface processes that erode or bury fault evidence. This study shows that tectonic landforms and lithologic age have a significant impact on fault location uncertainty and coseismic displacement, which should be considered in fault mapping and fault displacement assessment.

Abstract

Nickel stable isotopes (δ60Ni) provide insight to Ni biogeochemistry in the modern and past oceans. Here, we present the first Pacific Ocean high-resolution dissolved Ni concentration and δ60Ni data, from the US GEOTRACES GP15 cruise. As in other ocean basins, increases in δ60Ni toward the surface ocean are observed across the entire transect, reflecting preferential biological uptake of light Ni isotopes, however the observed magnitude of fractionation is larger in the tropical Pacific than the North Pacific Subtropical Gyre. Such surface ocean fractionation by phytoplankton should accumulate isotopically lighter Ni in the deep Pacific, yet we find that North Pacific deep ocean δ60Ni is similar to previously reported values from the deep Atlantic. Finally, we find that seawater dissolved δ60Ni in regions with hydrothermal input can be either higher or lower than background deep ocean δ60Ni, depending on vent geochemistry and proximity.

Abstract

Increasing impacts of wildfires on Western US air quality highlights the need for forecasts of smoke emissions based on dynamic modeled wildfires. This work utilizes knowledge of weather, fuels, topography, and firefighting, combined with machine learning and other statistical methods, to generate 1- and 2-day forecasts of fire radiative energy (FRE). The models are trained on data covering 2019 and 2021 and evaluated on data for 2020. For the 1-day (2-day) forecasts, the random forest model shows the most skill, explaining 48% (25%) of the variance in observed daily FRE when trained on all available predictors compared to the 2% (<0%) of variance explained by persistence for the extreme fire year of 2020. The random forest model also shows improved skill in forecasting day-to-day increases and decreases in FRE, with 28% (39%) of observed increase (decrease) days predicted, and increase (decrease) days are identified with 62% (60%) accuracy. Error in the random forest increases with FRE, and the random forest tends toward persistence under severe fire weather. Sensitivity analysis shows that near-surface weather and the latest observed FRE contribute the most to the skill of the model. When the random forest model was trained on subsets of the training data produced by agencies (e.g., the Canadian or US Forest Services), comparable if not better performance was achieved (1-day R 2 = 0.39–0.48, 2-day R 2 = 0.13–0.34). FRE is used to compute emissions, so these results demonstrate potential for improved fire emissions forecasts for air quality models.

Abstract

The last deglaciation experienced the retreat of massive ice sheets and a transition from the cold Last Glacial Maximum to the warmer Holocene. Key simulation challenges for this period include the timing and extent of ice sheet decay and meltwater input into the oceans. Here, major uncertainties and forcing factors for the last deglaciation are evaluated. Two sets of transient simulations are performed based on the novel ice-sheet reconstruction PaleoMist and the more established GLAC1D. The simulations reveal that the proximity of the Atlantic meridional overturning circulation (AMOC) to a bifurcation point, where it can switch between on- and off-modes, is primarily determined by the interplay of greenhouse gas concentrations, orbital forcing and freshwater forcing. The PaleoMist simulation qualitatively replicates the Bølling-Allerød (BA)/Younger Dryas (YD) sequence: a warming in Greenland and Antarctica during the BA, followed by a cooling northern North Atlantic and an Antarctic warming during the YD.

Abstract

During the 3 month long eruption of Kı̄lauea volcano, Hawaii in 2018, the pre-existing summit caldera collapsed in over 60 quasi-periodic failure events. The last 40 of these events, which generated Mw > 5 very long period (VLP) earthquakes, had inter-event times between 0.8 and 2.2 days. These failure events offer a unique data set for testing methods for predicting earthquake recurrence based on locally recorded GPS, tilt, and seismicity data. In this work, we train a deep learning graph neural network (GNN) to predict the time-to-failure of the caldera collapse events using only a fraction of the data recorded at the start of each cycle. We find that the GNN generalizes to unseen data and can predict the time-to-failure to within a few hours using only 0.5 days of data, substantially improving upon a null model based only on inter-event statistics. Predictions improve with increasing input data length, and are most accurate when using high-SNR tilt-meter data. Applying the trained GNN to synthetic data with different magma-chamber pressure decay times predicts failure at a nearly constant stress threshold, revealing that the GNN is sensing the underling physics of caldera collapse. These findings demonstrate the predictability of caldera collapse sequences under well monitored conditions, and highlight the potential of machine learning methods for forecasting real world catastrophic events with limited training data.